Regulating Social Influence Bias in Social Recommendation: Causal Disentanglement Approach

Core Concepts

The author proposes a Causal Disentanglement-based framework, CDRSB, to regulate social influence bias in social recommendation systems by disentangling interest and social influence embeddings. This approach aims to enhance recommendation performance.

Abstract

The content discusses the issue of social influence bias in social recommendation systems and introduces a novel framework, CDRSB, to address this problem. By disentangling user and item embeddings into interest and social influence components, the model aims to improve recommendation accuracy. Experimental results on real-world datasets demonstrate the effectiveness of CDRSB compared to existing baselines.
The paper highlights that not all biases are detrimental, as some recommendations from friends align with user interests. Blindly eliminating biases may lead to loss of essential information. The proposed method seeks to regulate social influence bias while preserving its positive effects.
Key points include:

Addressing social influence bias in recommendation systems.
Proposal of CDRSB framework for disentangling interest and social influence embeddings.
Importance of regulating bias without sacrificing meaningful recommendations.
Experimental validation on real-world datasets showcasing the effectiveness of CDRSB.

Stats

RMSE: 0.8246, 0.5950, 0.9404, 0.7078
MAE: 0.6759, 0.5128, 0.6695, 0.5277

Quotes

"We propose a causal disentanglement-based framework for regulating social influence bias in social recommendations."
"Some items recommended by friends may align with the user’s interests and deserve to be recommended."

Key Insights Distilled From

Causal Disentanglement for Regulating Social Influence Bias in Social Recommendation

by Li Wang,Min ... at arxiv.org 03-07-2024

https://arxiv.org/pdf/2403.03578.pdf

Deeper Inquiries

How can the proposed framework adapt to dynamic changes in user preferences over time

The proposed framework can adapt to dynamic changes in user preferences over time by incorporating a feedback loop mechanism. This mechanism allows the model to continuously update and refine user and item embeddings based on new interactions and feedback from users. By leveraging historical data, the model can track changes in user preferences, identify patterns, and adjust recommendations accordingly. Additionally, the disentangled encoder component of the framework enables the separation of interest and social influence embeddings, providing flexibility to capture evolving user preferences accurately. The regulatory decoder module dynamically calculates weights for social influence embeddings, allowing for real-time adjustments based on changing user behaviors.

What ethical considerations should be taken into account when leveraging social influence in recommendation systems

When leveraging social influence in recommendation systems, several ethical considerations should be taken into account to ensure responsible use of this information. Firstly, transparency is crucial - users should be informed about how their social connections are being utilized to personalize recommendations. Consent must also be obtained before using social network data for recommendation purposes. Additionally, it's essential to prioritize user privacy and data security by implementing robust measures to protect sensitive information shared within social networks. Bias mitigation strategies should be employed to prevent discriminatory or harmful outcomes resulting from biased recommendations influenced by social connections.

How might cultural differences impact the effectiveness of regulating social influence bias in diverse user populations

Cultural differences can significantly impact the effectiveness of regulating social influence bias in diverse user populations within recommendation systems. Different cultural norms and values may shape how individuals perceive recommendations from friends or acquaintances within their social networks. For example, some cultures may place a higher emphasis on collective decision-making or group consensus when making choices compared to individual preferences prevalent in other cultures.
Moreover, cultural diversity could affect the interpretation of what constitutes positive versus negative influences from peers regarding product recommendations.
To address these challenges effectively:

Tailoring regulation mechanisms: Customizing weight calculation algorithms based on cultural nuances could help optimize bias regulation according to specific cultural contexts.
Cultural sensitivity training: Ensuring that developers understand diverse cultural perspectives can aid in designing more inclusive recommendation systems that respect varying beliefs and values.
User feedback mechanisms: Implementing feedback loops where users can provide input on recommended items influenced by their social circles helps fine-tune bias regulation strategies across different cultural groups.
By considering these factors thoughtfully, recommendation systems can better navigate cross-cultural complexities while regulating social influence biases responsibly across diverse user populations.